Ponce Cruz, PedroMata Juárez, Omar2023-05-062023-05-062022-12-01Mata Juárez, O. (2022). 3D Computer vision for online activity detection. Case study: metabolic rate estimation for connected thermostat. [Unpublished doctoral thesis]. Instituto Tecnológico y de Estudios Superiores de Monterrey. Recuperado de: https://hdl.handle.net/11285/650676https://hdl.handle.net/11285/650676https://orcid.org/0000-0001-7035-5286The ability to detect human activities in computer vision has gained importance over the years due to its potential in many applications such crime prevention, healthcare, public safety, human-computer/robot interaction, smart homes, videogames, monitoring, etc. A way to achieve those applications is by doing a Human Activity Recognition (HAR) process in which an activity is identified by a series of physical actions that construct one physical activity. The identification requires sensors to obtain the data for processing and classifying it. These kinds of sensors are often found inside a smart home. Therefore, it is proposed to use noninvasive sensors in combination with digital signal processing to develop a platform for detecting human activity. Moreover, a case study is proposed for validating the platform by proposing a strategy to save energy on HVAC systems without affecting the thermal comfort of the occupantTextoengopenAccesshttp://creativecommons.org/licenses/by/4.0INGENIERÍA Y TECNOLOGÍA::CIENCIAS TECNOLÓGICAS::TECNOLOGÍA DE LOS ORDENADORES::INTELIGENCIA ARTIFICIALTechnologyTesis de doctoradohttps://orcid.org/0000-0002-2432-358X3D computer visionHuman Activity RecognitionPlatform-based productEnergy savingsConnected thermostat46550657203510219